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Scale and Generalization
Published in Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard, Thematic Cartography and Geovisualization, 2022
Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard
One research area that continues is around measurement. Hanna Stigmar and Lars Harrie (2011) develop and utilize a series of measures for describing the legibility of maps. The three types of measures in their study included: (1) the amount of information, which is determined by the amount and size of map objects; (2) the spatial distribution, which is determined by the density and distribution of map objects; and (3) the object complexity. Measures of the amount of information were typified by a number of objects, number of vertices, object line length, and object area. Spatial distribution measures included spatial distribution of objects, spatial distribution of vertices, degree of overlap, number of neighbors, and local density. Complexity measures were represented by object size, line segment size, angularity, line connectivity, and polygon shape. After applying these measures to several maps (at scales of 1:50,000 and 1:10,000), Stigmar and Harrie concluded that the measures that showed the best correspondence between perceived legibility and calculated legibility were the number of vertices, object line length, local density, proximity indicator, and degree of overlap. They also discovered that a combination of measures can provide a better reflection of the legibility than a single measure.
Stochastic computational modeling of heterogeneous poroelastic media
Published in J.-L. Auriault, C. Geindreau, P. Royer, J.-F. Bloch, C. Boutin, J. Lewandowska, Poromechanics II, 2020
D. Frias, M. A. Murad, F. Pereira
In what follows we consider a poroelastic heterogeneous oil reservoir where the absolute permeability K varies in an irregular manner. Whence, its spatial distribution is not known exactly and it is customary to regard it as a random space function. Hereafter, we only consider formations of isotropic heterogeneity by assuming that K has isotropic statistics and therefore it can be treated as a scalar random field. It should be noted that heterogeneities in the mechanical properties, such as the Lamé poroelastic constants, may also play an important role in reservoir geomechanics. However, we shall concentrate our analysis on the simplest problem, where the effects of heterogeneity are only manifested in K. Consequently, the effects of heterogeneity upon the poromechanical behavior are driven only by hydro-mechanical coupling.
Image Interpretation Keys
Published in Élizabeth L. Simms, SAR Image Interpretation for Various Land Covers, 2019
The pattern expresses a repetitive sequence of tone changes. This interpretation key is applicable where the spatial variation of tones is caused by objects that can be visually separated from each other, delineated, and potentially identified. Comparatively, a texture is formed by objects whose dimensions are less or about equal to the image spatial resolution. For example, where ground is covered with permanent crops and trees grow in one or two meter apart rows, a high spatial resolution image may lend a dotted pattern where each dot represents a tree (Figure 3.5). Low spatial resolution image of a similar type of crops would display a coarse texture from a mix of ground and trees contrasted backscattering, and radar shadow. The terminology for describing a pattern may evoke familiar forms such as dotted, grid, mottled (Figure 3.6), patchwork (Figure 3.7), ribbed, speckled (Figure 3.8), striped (Figure 3.9), or tiled. The more contrasted image tones are the easier is the pattern assessment. Whether a specific description is proposed as the interpretation key of land cover for a particular area, it is useful to consider overall patterns or spatial arrangement of several side-by-side entities. To observe that an ensemble is presenting a regular or irregular pattern (Figure 3.10) does provide information, for example, about terrain topography constraints, hydrology and other environmental parameters, or anthropological design practices. As an overarching description, patterns may evoke the spatial distribution such as dispersed, random, or clustered. These generic categories, that can be visually and quantitatively assessed (McGrew, Lembo, and Monroe 2014), convey LULC information of interest in geographical studies (Simms 2017).
Spatial pattern of tourist attractions and its influencing factors in China
Published in Journal of Spatial Science, 2020
Ting Wang, Lu Wang, Zhi-Zhong Ning
Tourist attractions can be seen as a series of geographical points on a small scale across space (Wang and He 2008). The spatial distribution pattern of geographical points has three basic modes: random distribution, aggregated distribution and uniform distribution. Quantitative methods can be applied to explain the spatial pattern of geographical points and to study the natural and socio-economic factors in distribution mode (Yang et al. 2016). The nearest-neighbour distance method, the multi-distance spatial clustering method, kernel density estimation and the multivariable linear regression model were common methods used in these studies. The nearest-neighbour distance method and the multi-distance spatial clustering method were often used to observe the random distribution or aggregation distribution of objects within the spatial range. First, we adapted the nearest-neighbour distance method to analyse the spatial distribution pattern of A-grade tourist attractions and identified the aggregated distribution phenomenon. Second, we verified the aggregated distribution phenomenon by means of the multi-distance spatial clustering method. Third, we visualized the aggregated distribution phenomenon via the kernel density estimation method. Finally, with the help of the correlation multivariate linear regression analysis model, we analysed the factors influencing agglomeration.
Spatial analysis of mortality rate of pedestrian accidents in Iran during 2012–2013
Published in Traffic Injury Prevention, 2019
Jalil Hasani, Saeed Erfanpoor, Abdolhalim Rajabi, Abdolrazagh Barzegar, Mahmood Khodadoost, Mohamad Afkar, Seyed Saeed Hashemi Nazari
Spatial autocorrelation is an indicator for determining the correlation and association between the variables in distant and near areas and can determine the model of pedestrian mortality rate in different regions. These indices are divided into 2 categories: global indices and local indices. Global indices include the global Moran’s I index and Getis-Ord general/global G statistic, which consider all of the data as a set and calculate mean of the total data and then compare the data for each area with the total mean; hence, the distribution of variables in the whole region, as a random spatial unit or with a specific pattern, and the distribution of data (cluster, dispersed, and random) can be determined. The local indices include the local Getis index or local indicators of spatial autocorrelation and local Getis Gi, which determine the distribution model of variables at the level of spatial units separately by comparing the data for each area with their adjacent points. In general, there are 3 distinct patterns of spatial distribution: cluster, random, and scattered (a description of these 3 patterns is provided in Appendix A, see online supplement).
Study of silver electrodeposition in deep eutectic solvents using atomic force microscopy
Published in Transactions of the IMF, 2018
A. P. Abbott, M. Azam, K. S. Ryder, S. Saleem
The local environment around a growing island has a very strong influence on its growth. The analysis of the islands’ spatial distribution can provide an insight into the kinetics of nucleation and growth. The spatial distribution may be random, cluster or dispersed. The key feature of the models proposed for the nucleation and growth of the islands is complete spatial randomness (CSR).25 CSR assumes that the probability of nucleation occurring at a position on the surface is the same for all positions.